aiming at prize for brilliant idea the world is not ready for

Sex http://www.ofai.at/rascalli/

Drugs http://www-tsujii.is.s.u-tokyo.ac.jp/medie/

What? Who? Why? Information Extraction (Text2Record) & Fact Gathering by Rules, Patterns, Learning, Linguistics, Statistics Cimple / DBlife (U Wisconsin) KnowItAll / TextRunner (U Washington) Avatar (IBM) YAGO-NAGA (MPI) Google, Microsoft, Yahoo, ... ... Most new knowledge first produced in NL text (news, blogs, scientific/business articles, dossiers, wikipedia) Lift text-borne content into value-added „semantic DB“ (entities & relations, RDF)

Sex: Scale Matters Can we correctly extract all worthy facts at the scale and rate by which news, blogs, gossips are produced ? Benchmark Challenge: for all people in Wikipedia (100,000‘s) gather all spouses, incl. divorced & widowed, and corresponding time periods! >95% accuracy, >95% coverage, in one night redundancy of sources helps, stresses scalability even more consistency constraints are potentially helpful: FD‘s: {husband, time}  wife, {wife, time}  husband inclusion dependencies: marriedPerson  adultPerson age/time/gender restrictions: birthdate +  < marriage < divorce

Databases (Know-How) scalability and system-oriented approach coping with trade-offs: precision vs. recall, speed vs. quality, freshness vs. accuracy quality boosting and efficiency gains by consistency constraints capturing & tracking confidence and lineage showcase for prob DB (more tractable than general case) killer app for RDF graph data

Conclusion Information extraction (text2data) is a major research avenue. It‘s sexy, and should get you hooked! DB-system know-how is vital!